Abstract
We investigate the multi-step prediction of the drivable space, represented by Occupancy Grid Maps (OGMs),
for autonomous vehicles. Our motivation is that accurate multi-step prediction of the drivable space can effi-
ciently improve path planning and navigation resulting in
safe, comfortable and optimum paths in autonomous driving. We train a variety of Recurrent Neural Network (RNN)
based architectures on the OGM sequences from the KITTI
dataset. The results demonstrate significant improvement
of the prediction accuracy using our proposed difference
learning method, incorporating motion related features,
over the state of the art. We remove the egomotion from
the OGM sequences by transforming them into a common
frame. Although in the transformed sequences the KITTI
dataset is heavily biased toward static objects, by learning the difference between consecutive OGMs, our proposed method provides accurate prediction over both the
static and moving objects. A video of the performance of
our method on the KITTI dataset is available at https:
//youtu.be/Bskd0Z7eLFE.